Final Assessment

Final Assessment Overview

  • Date & Time: 08/11/2024 10:00 am - 12:10 am

  • You should all have received information about Exam times. If not, contact the exam team immediately!

  • Exam administration is done centrally. Unfortunately we can not help you with any issues regrading scheduling, etc.

ETC1010 (Check your Exam timetable for the most up-to-date details!)

  • Location 1: Monash College, 49 Rainforest Walk, Clayton
  • Location 2: Room 101, LTB, Clayton

ETC5510 (Check your Exam timetable for the most up-to-date details!)

  • Location 1: Room 121, LTB, Clayton
  • Location 2: Room 101, LTB, Clayton

Final Assessment Format

The final assessment will be an e-assessment on campus.

Rules

  • All answers should be written as sentences. Please do not use bullet points (even though some of the practice exam solutions have bullet points)
  • No calculators! They are not needed.
  • No cheat sheet (We will confirm this later this week).
  • You don’t need to write any code.
  • You get up to 5 blank pieces of working paper

Topics

  • All topics from week 1 to week 11 are covered.
  • Except for those we identified as non-examinable during the lectures, such as regular expressions.
  • Focus on the methodology and the interpretation rather than the implementation in R.

Final Assessment Format

Format

  • There are 60 marks for undergrad students - so 2 min per mark on average
  • Post-grad students (ETC5510) have an extra 5 mark question
    • Why? It is a requirement of the University’s accreditation for assessments > 20%
  • The format is the same as the practice exam
  • Questions will appear like a quiz
  • There are some T/F MCQ questions
  • Most are short answer

Final Assessment Format

Expectation for answers

  • Generally 1-3 mark questions just need a short sentence or two in answer to a specific question
  • Some questions worth > 3 marks may involve a list, so short sentences ar OK here too
  • Questions that ask you to discuss or justify your answer
    • These require a bit more thought
    • They also are open, so any sensible discussion is OK
    • You need to be able to explain your reasoning
  • Hopefully you realise that the formatting and expectations are similar to A2
  • Remember this when you do the practice exam

Final Assessment Advice

  • Read the questions carefully in reading time
  • Identify the questions that you think you know and do those first
  • This should leave time to think about the other questions
  • If you get stuck - move on and return.
  • Worst case - just write something and you may be lucky
  • Blank answers get 0, but an attempt may not!

Exam Consultations

  • Use this, especially to check you practice exam answers
  • Check the timetable on Moodle (will release soon)

SETU

Student Survey

Please take 5-10 minutes to complete the Student Evaluation of Teaching and Units (SETU).

  • Your feedback is invaluable in helping us improve our teaching methods and course design.

  • Both the teaching team and the faculty place great importance on student input, so your participation is greatly appreciated.

EBS Honours

EBS Honours Degree

  • The Department of Econometrics and Business Statistics offers a one-year Honours program for outstanding students (WAM > 70%).

  • Three streams offered: Business Analytics, Actuarial Studies and Econometrics and Business Statistics

  • Benefits of the Honours program:

    • Many top employers (e.g. Reserve Bank of Australia) recruit Honours graduates.
    • Highly recommended for students planning to pursue a PhD or MPhil.
    • Upon completion, you can receive 1-year credit toward a Master of Commerce (MCom).

  • It’s an excellent first step toward academic research.

  • Econometrics Honours Memorial Scholarship available (AUD 15,000).

  • Join an excellent cohort of smart and creative students.

  • Opportunity to work with inspiring faculty members.

EBS Honours Degree

  • Program components:
    • 6 units of study (depending on your stream, negotiable)
    • 1 research project

Research milestons (2020 version)

  1. Week 4, Semester 1: Confirm research topic and supervisors.
  2. Week 9, Semester 1: Submit research plan, including a literature review.
  3. Week 10, Semester 1: Deliver the first presentation (15 minutes).
  4. Week 1, Semester 2: Submit preliminary draft of work in progress.
  5. Week 9, Semester 2: Submit draft of the research paper.
  6. Week 10, Semester 2: Deliver the second presentation (30 minutes).
  7. Week 12, Semester 2: Submit the final research paper.

Course Recap

EDA (Exploratory Data Analysis)

  • We explored the EDA process in detail.
  • The entire unit focuses on EDA.
  • It’s important to understand the goals and aims of EDA.
  • Be aware of what EDA can and cannot accomplish.
  • Use your lecture notes as a guide to help you explain the EDA process in your own words!

EDA: Tidy Data

  • What is tidy data?
  • Why is tidy data important?
  • What functions can you use to transform your data into a tidy format?

EDA: Reproducible Analysis

  • You have been using Quarto/RMarkdown for some time.
  • Why is reproducible analysis important?
  • What should we be cautious about when using Quarto/RMarkdown for reproducible practices?

EDA: Visualization

  • Consider the various types of visualizations and their corresponding functions.
  • What are the best practices and common pitfalls in visualization? How do we evaluate the quality of a visualization?
  • What questions can specific graphs help answer?
  • Focus on the interpretation of the information presented by the statistical graphics.

Relational Data

  • What is relational data?
  • What are the different types of keys?
  • What are the different types of joins, and how do they differ from one another?

Web Scraping

  • This topic is non-examinable.
  • How do we locate the desired node on a webpage?
  • How can we extract text using regular expressions?
  • What methods can we use to automatically scrape multiple pages?

Text Analysis

  • Regular expressions and metacharacters (non-examinable)
  • What is the tidy text format?
  • What is a text token?
  • What units of text are typically used as tokens?
  • What are stop words?
  • What is sentiment analysis?
  • How do we evaluate word importance? What statistics can we use, and what do they measure?

Cluster Analysis

  • What is cluster analysis, and how does it relate to EDA?
  • What are the limitations of cluster analysis?
  • What is hierarchical clustering, and what are the two types of hierarchical clustering? How do they differ?
  • What are the common distance measures used in clustering?
  • What are the common linkage methods for hierarchical clustering?

Cluster Analysis

  • Describe in your own words how k-means and hierarchical clustering work. What are the steps involved in conducting each method?
  • How do we determine the number of clusters for k-means and hierarchical clustering? How do we interpret a dendrogram?
  • Understand the advantages and disadvantages of k-means and hierarchical clustering:
    • K-means is generally faster.
    • The agglomerative approach is generally faster than the divisive approach.
    • K-means involves randomness, while hierarchical clustering does not.
    • …
  • How to visualize clustering result in high-dimensional data space? What should be checked?

Modeling

  • Correlation vs. Causation
  • What is a linear model?
  • Why do we include an error term in the formula?
  • What are the model assumptions, and what are the assumptions regarding the error term?
  • How do we interpret the estimated coefficients and predicted/fitted values? (This is extremely important for the correct interpretation of the model.)
  • Why do we use the term “average” frequently in our interpretations?

Modeling

  • What plots should be created to check model assumptions?
  • What constitutes a good residual plot, and what should we check for?
    • Patterns
    • Variance
    • Normality
    • …
  • What statistics can be used and should be consistently checked to assess the quality of the fit?
  • How do we handle categorical variables, and how do we interpret coefficients for those variables?
  • How do we perform model selection?

R Code

  • While R code isn’t required for the exam, it’s important to know how to use it.
  • You do need to know which package we heavily rely on for each topic for the exam! (e.g. tidytext for text analysis and broom for linear modeling)
  • Master dplyr and ggplot2, these two will be your most used packages in data analysis.
  • You may encounter a small code example where you’ll need to explain its functionality (similar to the practice exam).
  • Remember to practice, practice, and practice!

Finally

  • Double check your grades and let us know of any issues BEFORE the end of Swot Vac.

  • Check A2 immediately and let us know of any issues BEFORE the exam if possible.

  • If not, straight after the exam - marks will be finalised a week after the exam

  • The weekly quiz grades are 0 or 1, best 10/11. So the actual grade doesn’t count - you just had to attempt it!

  • Well done on making it through the semester

  • Good luck on the exam!

Thank You!

Any Questions?

ETC1010/ETC5510 Lecture 12 | Melbourne time